Land Change Analysis Tool (LCAT)

We employ a random forest machine learning classifier to produce high resolution land cover maps from aerial and/or satellite imagery. Training data is generated from a custom-built web application. We built and operate a 192-node docker cluster to parallelize CPU-intensive processing tasks. We are publishing results through a publicly available Image service. To date, we have mapped over 600 million acres and have generated over 700 thousand training samples.